datavalidation

Data validation is the process of ensuring data conforms to defined rules and quality criteria before it is stored, processed, or used. It aims to ensure accuracy, integrity, completeness, timeliness, and consistency of data across systems.

Validation can occur at multiple layers, including data entry, data transport, and data storage. Syntactic validation checks format and type; semantic validation checks business rules and contextual meaning; structural validation checks conformance to a schema or model.

Common techniques include type checks, range checks, format checks (for example, dates or emails), presence checks (not null), uniqueness constraints, and referential integrity. Cross-field validation verifies relationships between fields, such as end dates after start dates or totals matching component values.

Applications span user interfaces, batch processing, data integration, and data warehousing. In databases, constraints such as NOT NULL, CHECK, UNIQUE, and foreign key enforce validation at the data layer. In APIs, schema validation ensures incoming payloads conform to defined structures.

Validation is distinct from verification and data quality management. Validation asks whether data meets prescribed rules; verification assesses whether data accurately reflects its source; data quality programs combine profiling, monitoring, and remediation to maintain trustworthy data.

Practices emphasize early validation, clear rule definitions, non-disruptive error handling, and traceability of data issues. Automated validation, testing, and documentation help maintain consistency as systems evolve.